Although several classical LDL algorithms [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]} are proposed to tackle the task of modeling the feature space into the label space, these algorithms usually favor an accurate and ample feature space.
Briefly, these algorithms expect to conduct a process of condensing the representation space rather than augmenting it.
Here, we define a lemma that a feature space of dimension \(\approx \)  the label space is a small-scale feature space.
A shred of evidence is that almost all the proposed LDL algorithms report weak performance on benchmark datasets with a large number of labels in many studies.
So far, we draw two questions about this: 1) For the label space, is it difficult for the comparatively small amount of feature information to provide the algorithm with effective features to regress an accurate label distribution?
2) For the feature space, are there artificial reasons and uncertainty of the feature extractor that cause the low quality of the feature space?
Unfortunately, we cannot parse the existing LDL dataset because the details of feature processing are blind-boxed.
Further, we want to boost the feature dimension and infer the uncertainty of the feature space by tapping into expert knowledge is costly.
To solve the above two problems, we propose a feature augmentation technique with uncertainty awareness enforced on TabMixer (Tabular MLP-Mixer) to learn an LDL dataset with small-scale features.
Note that our network treats tabular data equally and does not distinguish between logical and continuous values.
